1. Field of the Invention
The present invention generally relates to analytic visualizations. More specifically, the present invention relates to the secure transfer of data for embedded analytic visualizations.
2. Description of the Related Art
With the continued proliferation of computing devices and the ubiquitous increase in Internet connectivity, dealing with vast quantities of data has become a norm in business and consumer markets. Viewing and manipulating such data while the data is still arranged in spreadsheets, tables, databases, and other data structures can often be slow, difficult, unwieldy, and in some cases, entirely unmanageable. Therefore, it is often helpful to arrange such data into analytic visualizations, such as charts and graphs. Typically, a user of spreadsheet software such as Microsoft Excel might manually import data through a data structure conversion process to generate a chart or graph from the data. The user may then export the chart or graph as a static image into a document or web page.
One problem with manually exporting analytic visualizations through spreadsheet software as static images is that there is no easy way to update, filter, interact with, or manipulate those visualizations if they are embedded into a web portal or similar medium where a viewer might expect data to be updated and interactive. In order to update such a static-image analytic visualization, someone must enter updated data into a spreadsheet, generate a new analytic visualization based on the updated data, export the updated analytic visualization as a new image, and embed the image into the web portal. Similarly, if a viewer would like to filter data in the visualization (e.g., view sales data for the United States when viewing an analytic visualization showing worldwide sales data), the data owner would need to generate, export, and embed a separate analytic visualization with the filtered data.
A further problem is that charts with any form of update mechanism are not designed to access data in a secure manner. Owners of data must often blindly trust third parties with their data to allow it any semblance of interactivity. Owners transfer large amounts of potentially sensitive data to third-party servers for processing, thereby giving rise to the possibility that the third party will sell or leak the data. Any sensitive data on such third party servers is further vulnerable to malicious hackers or snooping governmental entities if the network connections are compromised via a man-in-the-middle attack or if the third party servers themselves are compromised.
Similar problems exist with respect to personalization based on viewer permissions. Presently available systems display charts with the same level of detail and same viewable categories of data when displaying to a high-ranking company executive as to a lower-level company employee or to a member of the public. Nor do presently available systems allow for viewer interactivity with charts that update based on viewer actions and viewer inputs from the viewer of the chart. Accessing data from multiple sources is also a challenge, especially with respect to data security.
There is, therefore, a need in the art for improved analytic visualization systems that maintain security while allowing for different levels of permissions, interactivity, and integration of data from multiple data sources.